Traditional computers can solve some quantum problems

Traditional computers can solve some quantum problems

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There’s been a lot of buzz about quantum computers, and for good reason. Futuristic computers are designed to mimic what happens in nature at microscopic scales, which means they have the power to better understand the quantum realm and accelerate the discovery of new materials, including pharmaceuticals, environmentally friendly chemicals, etc. However, experts say viable quantum computers are still a decade or more away. What should researchers do in the meantime?

A new Caltech-led study in the journal Science describes how machine learning tools, running on classical computers, can be used to make predictions about quantum systems and thereby help researchers solve some of the trickiest problems in physics and chemistry. Although this notion has already been demonstrated experimentally, the new report is the first to mathematically prove that the method works.

“Quantum computers are ideal for many kinds of physics and materials science problems,” says lead author Hsin-Yuan (Robert) Huang, a graduate student working with John Preskill, the Richard P. Feynman Professor of Physics theory and the Allen VC Davis and Lenabelle Davis Institute of Quantum Science and Technology (IQIM) Leadership Chair. “But we’re not there yet, and we were surprised to learn that conventional machine learning methods can be used in the meantime. Ultimately, this article aims to show what humans can learn about the physical world.”

At microscopic levels, the physical world becomes an incredibly complex place governed by the laws of quantum physics. In this domain, the particles can exist in a superposition of states, or in two states at the same time. And a superposition of states can lead to entanglement, a phenomenon in which particles are linked, or correlated, without even being in contact with each other. These strange states and connections, prevalent in natural and man-made materials, are very difficult to describe mathematically.

“Predicting the low-energy state of a material is very difficult,” says Huang. “There are a huge number of atoms, and they’re layered and intertwined. You can’t write an equation to describe everything.”

The new study is the first mathematical demonstration that classical machine learning can be used to bridge the gap between us and the quantum world. Machine learning is a type of computer application that mimics the human brain to learn from data.

“We are classical beings living in a quantum world,” Preskill says. “Our brains and computers are classical, which limits our ability to interact with and understand quantum reality.”

While previous studies have shown that machine learning applications have the ability to solve some quantum problems, these methods generally work in ways that make it difficult for researchers to learn how machines arrived at their solutions.

“Normally, when it comes to machine learning, you don’t know how the machine solved the problem. It’s a black box,” Huang says. “But now we’ve basically figured out what’s going on inside the box thanks to our numerical simulations.” Huang and his colleagues performed extensive numerical simulations in conjunction with Caltech’s AWS Center for Quantum Computing, which corroborated their theoretical findings.

The new study will help scientists better understand and classify the complex and exotic phases of quantum matter.

“The concern was that people creating new quantum states in the lab might not be able to understand them,” Preskill says. “But now we can get reasonable classical data to explain what’s going on. Classical machines don’t just give us an oracle-like answer, but guide us to a deeper understanding.”

Co-author Victor V. Albert, a National Institute of Standards and Technology (NIST) physicist and former DuBridge Prize postdoctoral fellow at Caltech, agrees. “The part that excites me the most about this work is that we are now closer to a tool that helps you understand the underlying phase of a quantum state without requiring you to know a lot about that state at the moment. advance.”

Ultimately, of course, future quantum-based machine learning tools will outperform classical methods, the scientists say. In a related study published on June 10, 2022, in ScienceHuang, Preskill and their collaborators say they used Google’s Sycamore processor, a rudimentary quantum computer, to demonstrate that quantum machine learning is superior to classical approaches.

“We are still at the very beginning of this field,” says Huang. “But we know quantum machine learning will ultimately be the most effective.”

The Science The study is titled “Efficient Machine Learning for Quantum Many-Body Problems”.


The theory suggests that quantum computers should be exponentially faster on certain learning tasks than classical machines


More information:
Hsin-Yuan Huang, Efficient machine learning for quantum many-body problems, Science (2022). DOI: 10.1126/science.abk3333. www.science.org/doi/10.1126/science.abk3333

Provided by California Institute of Technology

Quote: Traditional Computers Can Solve Some Quantum Problems (2022, September 22) Retrieved September 23, 2022 from https://phys.org/news/2022-09-traditional-quantum-problems.html

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